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<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.loftr.transformer</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import copy

import torch
import torch.nn as nn
from silk.backbones.loftr.linear_attention import LinearAttention, FullAttention


class LoFTREncoderLayer(nn.Module):
    def __init__(self, d_model, nhead, attention=&#34;linear&#34;):
        super(LoFTREncoderLayer, self).__init__()

        self.dim = d_model // nhead
        self.nhead = nhead

        # multi-head attention
        self.q_proj = nn.Linear(d_model, d_model, bias=False)
        self.k_proj = nn.Linear(d_model, d_model, bias=False)
        self.v_proj = nn.Linear(d_model, d_model, bias=False)
        self.attention = LinearAttention() if attention == &#34;linear&#34; else FullAttention()
        self.merge = nn.Linear(d_model, d_model, bias=False)

        # feed-forward network
        self.mlp = nn.Sequential(
            nn.Linear(d_model * 2, d_model * 2, bias=False),
            nn.ReLU(True),
            nn.Linear(d_model * 2, d_model, bias=False),
        )

        # norm and dropout
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, x, source, x_mask=None, source_mask=None):
        &#34;&#34;&#34;
        Args:
            x (torch.Tensor): [N, L, C]
            source (torch.Tensor): [N, S, C]
            x_mask (torch.Tensor): [N, L] (optional)
            source_mask (torch.Tensor): [N, S] (optional)
        &#34;&#34;&#34;
        bs = x.size(0)
        query, key, value = x, source, source

        # multi-head attention
        query = self.q_proj(query).view(bs, -1, self.nhead, self.dim)  # [N, L, (H, D)]
        key = self.k_proj(key).view(bs, -1, self.nhead, self.dim)  # [N, S, (H, D)]
        value = self.v_proj(value).view(bs, -1, self.nhead, self.dim)
        message = self.attention(
            query, key, value, q_mask=x_mask, kv_mask=source_mask
        )  # [N, L, (H, D)]
        message = self.merge(message.view(bs, -1, self.nhead * self.dim))  # [N, L, C]
        message = self.norm1(message)

        # feed-forward network
        message = self.mlp(torch.cat([x, message], dim=2))
        message = self.norm2(message)

        return x + message


class LocalFeatureTransformer(nn.Module):
    &#34;&#34;&#34;A Local Feature Transformer (LoFTR) module.&#34;&#34;&#34;

    def __init__(self, config):
        super(LocalFeatureTransformer, self).__init__()

        self.config = config
        self.d_model = config[&#34;d_model&#34;]
        self.nhead = config[&#34;nhead&#34;]
        self.layer_names = config[&#34;layer_names&#34;]
        encoder_layer = LoFTREncoderLayer(
            config[&#34;d_model&#34;],
            config[&#34;nhead&#34;],
            config[&#34;attention&#34;],
        )
        self.layers = nn.ModuleList(
            [copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]
        )
        self._reset_parameters()

    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() &gt; 1:
                nn.init.xavier_uniform_(p)

    def forward(self, feat0, feat1, mask0=None, mask1=None):
        &#34;&#34;&#34;
        Args:
            feat0 (torch.Tensor): [N, L, C]
            feat1 (torch.Tensor): [N, S, C]
            mask0 (torch.Tensor): [N, L] (optional)
            mask1 (torch.Tensor): [N, S] (optional)
        &#34;&#34;&#34;

        assert self.d_model == feat0.size(
            2
        ), &#34;the feature number of src and transformer must be equal&#34;

        for layer, name in zip(self.layers, self.layer_names):
            if name == &#34;self&#34;:
                feat0 = layer(feat0, feat0, mask0, mask0)
                feat1 = layer(feat1, feat1, mask1, mask1)
            elif name == &#34;cross&#34;:
                feat0 = layer(feat0, feat1, mask0, mask1)
                feat1 = layer(feat1, feat0, mask1, mask0)
            else:
                raise KeyError

        return feat0, feat1</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.loftr.transformer.LoFTREncoderLayer"><code class="flex name class">
<span>class <span class="ident">LoFTREncoderLayer</span></span>
<span>(</span><span>d_model, nhead, attention='linear')</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LoFTREncoderLayer(nn.Module):
    def __init__(self, d_model, nhead, attention=&#34;linear&#34;):
        super(LoFTREncoderLayer, self).__init__()

        self.dim = d_model // nhead
        self.nhead = nhead

        # multi-head attention
        self.q_proj = nn.Linear(d_model, d_model, bias=False)
        self.k_proj = nn.Linear(d_model, d_model, bias=False)
        self.v_proj = nn.Linear(d_model, d_model, bias=False)
        self.attention = LinearAttention() if attention == &#34;linear&#34; else FullAttention()
        self.merge = nn.Linear(d_model, d_model, bias=False)

        # feed-forward network
        self.mlp = nn.Sequential(
            nn.Linear(d_model * 2, d_model * 2, bias=False),
            nn.ReLU(True),
            nn.Linear(d_model * 2, d_model, bias=False),
        )

        # norm and dropout
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, x, source, x_mask=None, source_mask=None):
        &#34;&#34;&#34;
        Args:
            x (torch.Tensor): [N, L, C]
            source (torch.Tensor): [N, S, C]
            x_mask (torch.Tensor): [N, L] (optional)
            source_mask (torch.Tensor): [N, S] (optional)
        &#34;&#34;&#34;
        bs = x.size(0)
        query, key, value = x, source, source

        # multi-head attention
        query = self.q_proj(query).view(bs, -1, self.nhead, self.dim)  # [N, L, (H, D)]
        key = self.k_proj(key).view(bs, -1, self.nhead, self.dim)  # [N, S, (H, D)]
        value = self.v_proj(value).view(bs, -1, self.nhead, self.dim)
        message = self.attention(
            query, key, value, q_mask=x_mask, kv_mask=source_mask
        )  # [N, L, (H, D)]
        message = self.merge(message.view(bs, -1, self.nhead * self.dim))  # [N, L, C]
        message = self.norm1(message)

        # feed-forward network
        message = self.mlp(torch.cat([x, message], dim=2))
        message = self.norm2(message)

        return x + message</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.transformer.LoFTREncoderLayer.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.transformer.LoFTREncoderLayer.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.transformer.LoFTREncoderLayer.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x, source, x_mask=None, source_mask=None) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><h2 id="args">Args</h2>
<dl>
<dt><strong><code>x</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, L, C]</dd>
<dt><strong><code>source</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, S, C]</dd>
<dt><strong><code>x_mask</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, L] (optional)</dd>
<dt><strong><code>source_mask</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, S] (optional)</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x, source, x_mask=None, source_mask=None):
    &#34;&#34;&#34;
    Args:
        x (torch.Tensor): [N, L, C]
        source (torch.Tensor): [N, S, C]
        x_mask (torch.Tensor): [N, L] (optional)
        source_mask (torch.Tensor): [N, S] (optional)
    &#34;&#34;&#34;
    bs = x.size(0)
    query, key, value = x, source, source

    # multi-head attention
    query = self.q_proj(query).view(bs, -1, self.nhead, self.dim)  # [N, L, (H, D)]
    key = self.k_proj(key).view(bs, -1, self.nhead, self.dim)  # [N, S, (H, D)]
    value = self.v_proj(value).view(bs, -1, self.nhead, self.dim)
    message = self.attention(
        query, key, value, q_mask=x_mask, kv_mask=source_mask
    )  # [N, L, (H, D)]
    message = self.merge(message.view(bs, -1, self.nhead * self.dim))  # [N, L, C]
    message = self.norm1(message)

    # feed-forward network
    message = self.mlp(torch.cat([x, message], dim=2))
    message = self.norm2(message)

    return x + message</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.loftr.transformer.LocalFeatureTransformer"><code class="flex name class">
<span>class <span class="ident">LocalFeatureTransformer</span></span>
<span>(</span><span>config)</span>
</code></dt>
<dd>
<div class="desc"><p>A Local Feature Transformer (LoFTR) module.</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LocalFeatureTransformer(nn.Module):
    &#34;&#34;&#34;A Local Feature Transformer (LoFTR) module.&#34;&#34;&#34;

    def __init__(self, config):
        super(LocalFeatureTransformer, self).__init__()

        self.config = config
        self.d_model = config[&#34;d_model&#34;]
        self.nhead = config[&#34;nhead&#34;]
        self.layer_names = config[&#34;layer_names&#34;]
        encoder_layer = LoFTREncoderLayer(
            config[&#34;d_model&#34;],
            config[&#34;nhead&#34;],
            config[&#34;attention&#34;],
        )
        self.layers = nn.ModuleList(
            [copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]
        )
        self._reset_parameters()

    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() &gt; 1:
                nn.init.xavier_uniform_(p)

    def forward(self, feat0, feat1, mask0=None, mask1=None):
        &#34;&#34;&#34;
        Args:
            feat0 (torch.Tensor): [N, L, C]
            feat1 (torch.Tensor): [N, S, C]
            mask0 (torch.Tensor): [N, L] (optional)
            mask1 (torch.Tensor): [N, S] (optional)
        &#34;&#34;&#34;

        assert self.d_model == feat0.size(
            2
        ), &#34;the feature number of src and transformer must be equal&#34;

        for layer, name in zip(self.layers, self.layer_names):
            if name == &#34;self&#34;:
                feat0 = layer(feat0, feat0, mask0, mask0)
                feat1 = layer(feat1, feat1, mask1, mask1)
            elif name == &#34;cross&#34;:
                feat0 = layer(feat0, feat1, mask0, mask1)
                feat1 = layer(feat1, feat0, mask1, mask0)
            else:
                raise KeyError

        return feat0, feat1</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.transformer.LocalFeatureTransformer.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.transformer.LocalFeatureTransformer.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.transformer.LocalFeatureTransformer.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, feat0, feat1, mask0=None, mask1=None) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><h2 id="args">Args</h2>
<dl>
<dt><strong><code>feat0</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, L, C]</dd>
<dt><strong><code>feat1</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, S, C]</dd>
<dt><strong><code>mask0</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, L] (optional)</dd>
<dt><strong><code>mask1</code></strong> :&ensp;<code>torch.Tensor</code></dt>
<dd>[N, S] (optional)</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, feat0, feat1, mask0=None, mask1=None):
    &#34;&#34;&#34;
    Args:
        feat0 (torch.Tensor): [N, L, C]
        feat1 (torch.Tensor): [N, S, C]
        mask0 (torch.Tensor): [N, L] (optional)
        mask1 (torch.Tensor): [N, S] (optional)
    &#34;&#34;&#34;

    assert self.d_model == feat0.size(
        2
    ), &#34;the feature number of src and transformer must be equal&#34;

    for layer, name in zip(self.layers, self.layer_names):
        if name == &#34;self&#34;:
            feat0 = layer(feat0, feat0, mask0, mask0)
            feat1 = layer(feat1, feat1, mask1, mask1)
        elif name == &#34;cross&#34;:
            feat0 = layer(feat0, feat1, mask0, mask1)
            feat1 = layer(feat1, feat0, mask1, mask0)
        else:
            raise KeyError

    return feat0, feat1</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.loftr" href="index.html">silk.backbones.loftr</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.loftr.transformer.LoFTREncoderLayer" href="#silk.backbones.loftr.transformer.LoFTREncoderLayer">LoFTREncoderLayer</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.transformer.LoFTREncoderLayer.dump_patches" href="#silk.backbones.loftr.transformer.LoFTREncoderLayer.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.transformer.LoFTREncoderLayer.forward" href="#silk.backbones.loftr.transformer.LoFTREncoderLayer.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.transformer.LoFTREncoderLayer.training" href="#silk.backbones.loftr.transformer.LoFTREncoderLayer.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.loftr.transformer.LocalFeatureTransformer" href="#silk.backbones.loftr.transformer.LocalFeatureTransformer">LocalFeatureTransformer</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.transformer.LocalFeatureTransformer.dump_patches" href="#silk.backbones.loftr.transformer.LocalFeatureTransformer.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.transformer.LocalFeatureTransformer.forward" href="#silk.backbones.loftr.transformer.LocalFeatureTransformer.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.transformer.LocalFeatureTransformer.training" href="#silk.backbones.loftr.transformer.LocalFeatureTransformer.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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